{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/gwf25/Desktop/Table 4.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Dec 2015, 10:07:26

{com}. do "/Users/gwf25/Dropbox/research/religion/final code/Table 4.do"
{txt}
{com}. clear all
{txt}
{com}. set mem 50m
{txt}(51200k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. drop id
{txt}
{com}. // End of variables
. 
. *Panel A: Amount contributed to public good
. rename s6 contribute
{txt}
{com}. 
. reg contribute treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      56
                                                       {txt}F(  1,    54) ={res}    0.01
                                                       {txt}Prob > F      = {res} 0.9206
                                                       {txt}R-squared     = {res} 0.0002
                                                       {txt}Root MSE      = {res}  .4276

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0115625{col 26}{space 2} .1154354{col 37}{space 1}    0.10{col 46}{space 3}0.921{col 54}{space 4}-.2198716{col 67}{space 3} .2429966
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5584375{col 26}{space 2} .0756476{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4} .4067732{col 67}{space 3} .7101018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg contribute treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     168
                                                       {txt}F(  1,   166) ={res}    3.52
                                                       {txt}Prob > F      = {res} 0.0623
                                                       {txt}R-squared     = {res} 0.0208
                                                       {txt}Root MSE      = {res}   .409

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .1186505{col 26}{space 2} .0632271{col 37}{space 1}    1.88{col 46}{space 3}0.062{col 54}{space 4}-.0061823{col 67}{space 3} .2434833
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4867816{col 26}{space 2} .0431224{col 37}{space 1}   11.29{col 46}{space 3}0.000{col 54}{space 4} .4016426{col 67}{space 3} .5719206
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Panel B: Expectations of others' contribution to public good
. rename s6exp expect
{txt}
{com}. 
. reg expect treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      56
                                                       {txt}F(  1,    54) ={res}    1.00
                                                       {txt}Prob > F      = {res} 0.3224
                                                       {txt}R-squared     = {res} 0.0179
                                                       {txt}Root MSE      = {res} .29443

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      expect{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0789583{col 26}{space 2} .0790543{col 37}{space 1}    1.00{col 46}{space 3}0.322{col 54}{space 4} -.079536{col 67}{space 3} .2374526
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .611875{col 26}{space 2} .0529254{col 37}{space 1}   11.56{col 46}{space 3}0.000{col 54}{space 4} .5057659{col 67}{space 3} .7179841
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg expect treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     168
                                                       {txt}F(  1,   166) ={res}    3.42
                                                       {txt}Prob > F      = {res} 0.0660
                                                       {txt}R-squared     = {res} 0.0203
                                                       {txt}Root MSE      = {res} .30581

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      expect{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0875181{col 26}{space 2} .0472926{col 37}{space 1}    1.85{col 46}{space 3}0.066{col 54}{space 4}-.0058545{col 67}{space 3} .1808906
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5248276{col 26}{space 2} .0320805{col 37}{space 1}   16.36{col 46}{space 3}0.000{col 54}{space 4} .4614892{col 67}{space 3}  .588166
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Panel C: Relationship between own contribution and exp of others' contribution
. reg contribute treatR expect if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      56
                                                       {txt}F(  2,    53) ={res}   47.92
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.4809
                                                       {txt}Root MSE      = {res}   .311

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.0679495{col 26}{space 2} .0867601{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-.2419682{col 67}{space 3} .1060693
{txt}{space 6}expect {c |}{col 14}{res}{space 2} 1.007012{col 26}{space 2} .1111369{col 37}{space 1}    9.06{col 46}{space 3}0.000{col 54}{space 4} .7840992{col 67}{space 3} 1.229924
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0577277{col 26}{space 2} .0606587{col 37}{space 1}   -0.95{col 46}{space 3}0.346{col 54}{space 4}-.1793937{col 67}{space 3} .0639383
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg contribute treatR expect if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     168
                                                       {txt}F(  2,   165) ={res}  112.42
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.5167
                                                       {txt}Root MSE      = {res} .28822

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  contribute{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0353576{col 26}{space 2} .0469034{col 37}{space 1}    0.75{col 46}{space 3}0.452{col 54}{space 4}-.0572506{col 67}{space 3} .1279659
{txt}{space 6}expect {c |}{col 14}{res}{space 2} .9517214{col 26}{space 2} .0736406{col 37}{space 1}   12.92{col 46}{space 3}0.000{col 54}{space 4} .8063221{col 67}{space 3} 1.097121
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.012708{col 26}{space 2} .0428548{col 37}{space 1}   -0.30{col 46}{space 3}0.767{col 54}{space 4}-.0973226{col 67}{space 3} .0719065
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. clear all
{txt}
{com}. set mem 30m
{txt}(30720k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. // End of variables
. 
. ***Drop those subjects who don't take part in the risk preference section
. drop if s3q1 == .
{txt}(596 observations deleted)

{com}. 
. ***Generate upper limit for risk with small amounts
. gen risk1=.
{txt}(406 missing values generated)

{com}. replace risk1=(1.6*0.5-1) if s3q1==1
{txt}(37 real changes made)

{com}. gen risk2=.
{txt}(406 missing values generated)

{com}. replace risk2=(2*0.5-1) if s3q2==1
{txt}(111 real changes made)

{com}. gen risk3=.
{txt}(406 missing values generated)

{com}. replace risk3=(2.4*0.5-1) if s3q3==1
{txt}(244 real changes made)

{com}. gen risk4=.
{txt}(406 missing values generated)

{com}. replace risk4=(2.8*0.5-1) if s3q4==1
{txt}(342 real changes made)

{com}. gen risk5=.
{txt}(406 missing values generated)

{com}. replace risk5=(3.2*0.5-1) if s3q5==1
{txt}(377 real changes made)

{com}. gen risk6=.
{txt}(406 missing values generated)

{com}. replace risk6=(3.6*0.5-1) if s3q6==1
{txt}(390 real changes made)

{com}. 
. ***Choose the upper limit for the first time a subject chooses the risky asset with small amounts.
. gen reservationrisk1= min(risk1,risk2,risk3,risk4,risk5,risk6)
{txt}(11 missing values generated)

{com}. 
. ***Generate upper limit for risk with large amounts
. gen risk7=.
{txt}(406 missing values generated)

{com}. replace risk7=(160*0.5-100)/100 if s4q1==1
{txt}(19 real changes made)

{com}. gen risk8=.
{txt}(406 missing values generated)

{com}. replace risk8=(200*0.5-100)/100 if s4q2==1
{txt}(51 real changes made)

{com}. gen risk9=.
{txt}(406 missing values generated)

{com}. replace risk9=(240*0.5-100)/100 if s4q3==1
{txt}(137 real changes made)

{com}. gen risk10=.
{txt}(406 missing values generated)

{com}. replace risk10=(280*0.5-100)/100 if s4q4==1
{txt}(224 real changes made)

{com}. gen risk11=.
{txt}(406 missing values generated)

{com}. replace risk11=(320*0.5-100)/100 if s4q5==1
{txt}(283 real changes made)

{com}. gen risk12=.
{txt}(406 missing values generated)

{com}. replace risk12=(360*0.5-100)/100 if s4q6==1
{txt}(298 real changes made)

{com}. 
. ***Choose the upper limit for the first time a subject chooses the risky asset with large amounts.
. gen reservationrisk2= min(risk7,risk8,risk9,risk10,risk11,risk12)
{txt}(102 missing values generated)

{com}. 
. ***Create two entries per subject. One is for small amounts and the other is for large amounts. riskchoice indicates whether it is a small or large stake gamble. 
. reshape long reservationrisk, i(id) j(riskchoice)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}     406   {txt}->{res}     812
{txt}Number of variables            {res}     218   {txt}->{res}     218
{txt}j variable (2 values)                     ->   {res}riskchoice
{txt}xij variables:
      {res}reservationrisk1 reservationrisk2   {txt}->   {res}reservationrisk
{txt}{hline 77}

{com}. 
. ***largestake is a dummy where a 1 indicates risk choices with large amounts and a 0 indicates risk choices with small amounts.
. gen largestake=0
{txt}
{com}. replace largestake= 1 if riskchoice==2 
{txt}(406 real changes made)

{com}. 
. ***Recall reservationrisk indicates the upper limit. Rename this variable risku and create another variable, riskl, which will indicate the lower limit.
. rename reservationrisk risk
{txt}
{com}. gen riskl=.
{txt}(812 missing values generated)

{com}. gen risku=risk
{txt}(113 missing values generated)

{com}. 
. ***Fill in values for the lower limit.
. ***Note that even if someone always chose the safe option, they are assigned a missing value for the upper limit and the highest possible value we ask about for the lower limit. So they are properly taken care of.
. replace riskl=. if risk < -.2
{txt}(0 real changes made)

{com}. replace riskl=(1.6*0.5-1) if risk == 0
{txt}(108 real changes made)

{com}. replace riskl=(2*0.5-1) if risk > .1 & risk < .3
{txt}(225 real changes made)

{com}. replace riskl=(2.4*0.5-1) if risk > .3 & risk < .5
{txt}(187 real changes made)

{com}. replace riskl=(2.8*0.5-1) if risk > .5 & risk < .7
{txt}(97 real changes made)

{com}. replace riskl=(3.2*0.5-1) if risk > .7 & risk < .9
{txt}(26 real changes made)

{com}. replace riskl=(3.6*0.5-1) if risk == . & s4q1 ~= .
{txt}(113 real changes made)

{com}. 
. ***Panel D: Risk Aversion
. intreg riskl risku treatR largestake if relig==3, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-141.33336}  
Iteration 1:{space 3}log pseudolikelihood = {res:-140.39271}  
Iteration 2:{space 3}log pseudolikelihood = {res:-140.38819}  
Iteration 3:{space 3}log pseudolikelihood = {res:-140.38819}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -132.7586}  
Iteration 1:{space 3}log pseudolikelihood = {res:-131.95742}  
Iteration 2:{space 3}log pseudolikelihood = {res: -131.9553}  
Iteration 3:{space 3}log pseudolikelihood = {res: -131.9553}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}        80
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}     15.28
{txt}Log pseudolikelihood = {res} -131.9553{txt}{col 51}Prob > chi2{col 67}= {res}    0.0005

{txt}{ralign 78:(Std. Err. adjusted for {res:40} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0214262{col 26}{space 2} .0570997{col 37}{space 1}    0.38{col 46}{space 3}0.707{col 54}{space 4}-.0904872{col 67}{space 3} .1333396
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .2601205{col 26}{space 2} .0672542{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .1283047{col 67}{space 3} .3919363
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0925979{col 26}{space 2}  .041181{col 37}{space 1}    2.25{col 46}{space 3}0.025{col 54}{space 4} .0118847{col 67}{space 3} .1733111
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2}-1.336027{col 26}{space 2} .1015675{col 37}{space 1}  -13.15{col 46}{space 3}0.000{col 54}{space 4}-1.535095{col 67}{space 3}-1.136958
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .2628881{col 26}{space 2} .0267009{col 54}{space 4} .2154351{col 67}{space 3} .3207934
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}        3{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}        7{col 34}{txt}right-censored observations
{col 24}{res}       70{col 34}{txt}      interval observations

{com}. 
. intreg riskl risku treatR largestake if relig==4, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-368.76763}  
Iteration 1:{space 3}log pseudolikelihood = {res:-364.20797}  
Iteration 2:{space 3}log pseudolikelihood = {res:-364.16362}  
Iteration 3:{space 3}log pseudolikelihood = {res:-364.16362}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -351.0699}  
Iteration 1:{space 3}log pseudolikelihood = {res:-346.64053}  
Iteration 2:{space 3}log pseudolikelihood = {res:-346.60402}  
Iteration 3:{space 3}log pseudolikelihood = {res:-346.60402}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}     44.75
{txt}Log pseudolikelihood = {res}-346.60402{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 78:(Std. Err. adjusted for {res:98} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} -.115996{col 26}{space 2} .0524186{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-.2187347{col 67}{space 3}-.0132574
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .2640933{col 26}{space 2} .0418926{col 37}{space 1}    6.30{col 46}{space 3}0.000{col 54}{space 4} .1819854{col 67}{space 3} .3462013
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1600286{col 26}{space 2} .0344244{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4} .0925581{col 67}{space 3} .2274991
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2}-1.147041{col 26}{space 2} .0674789{col 37}{space 1}  -17.00{col 46}{space 3}0.000{col 54}{space 4}-1.279297{col 67}{space 3}-1.014785
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .3175751{col 26}{space 2} .0214296{col 54}{space 4} .2782328{col 67}{space 3} .3624805
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}       12{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}       22{col 34}{txt}right-censored observations
{col 24}{res}      162{col 34}{txt}      interval observations

{com}. 
. intreg riskl risku treatR largestake if s10q15=="Agnostic" 

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-192.05699}  
Iteration 1:{space 3}log likelihood = {res:-190.53431}  
Iteration 2:{space 3}log likelihood = {res:-190.52463}  
Iteration 3:{space 3}log likelihood = {res:-190.52463}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-183.03943}  
Iteration 1:{space 3}log likelihood = {res: -181.6796}  
Iteration 2:{space 3}log likelihood = {res:-181.67343}  
Iteration 3:{space 3}log likelihood = {res:-181.67343}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}       106
{txt}{col 51}LR chi2({res}2{txt}){col 67}= {res}     17.70
{txt}Log likelihood = {res}-181.67343{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.1689815{col 26}{space 2} .0576934{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.2820584{col 67}{space 3}-.0559046
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .1876648{col 26}{space 2} .0576962{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} .0745823{col 67}{space 3} .3007473
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2223898{col 26}{space 2} .0500166{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} .1243592{col 67}{space 3} .3204205
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2}-1.243765{col 26}{space 2} .0790612{col 37}{space 1}  -15.73{col 46}{space 3}0.000{col 54}{space 4}-1.398722{col 67}{space 3}-1.088808
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .2882968{col 26}{space 2} .0227931{col 54}{space 4} .2469124{col 67}{space 3} .3366176
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}        7{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}        8{col 34}{txt}right-censored observations
{col 24}{res}       91{col 34}{txt}      interval observations

{com}. 
. intreg riskl risku treatR largestake if s10q15=="Atheist" 

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-175.05323}  
Iteration 1:{space 3}log likelihood = {res:-171.94352}  
Iteration 2:{space 3}log likelihood = {res:  -171.901}  
Iteration 3:{space 3}log likelihood = {res:-171.90099}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-164.44115}  
Iteration 1:{space 3}log likelihood = {res:-161.48622}  
Iteration 2:{space 3}log likelihood = {res:-161.45426}  
Iteration 3:{space 3}log likelihood = {res:-161.45425}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}        90
{txt}{col 51}LR chi2({res}2{txt}){col 67}= {res}     20.89
{txt}Log likelihood = {res}-161.45425{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.0485653{col 26}{space 2} .0749073{col 37}{space 1}   -0.65{col 46}{space 3}0.517{col 54}{space 4}-.1953808{col 67}{space 3} .0982503
{txt}{space 2}largestake {c |}{col 14}{res}{space 2} .3574793{col 26}{space 2} .0751751{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .2101389{col 67}{space 3} .5048197
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0845189{col 26}{space 2} .0650202{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0429183{col 67}{space 3} .2119561
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2}-1.066107{col 26}{space 2} .0906537{col 37}{space 1}  -11.76{col 46}{space 3}0.000{col 54}{space 4}-1.243785{col 67}{space 3}-.8884287
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} .3443466{col 26}{space 2} .0312163{col 54}{space 4} .2882911{col 67}{space 3} .4113015
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}        5{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}       14{col 34}{txt}right-censored observations
{col 24}{res}       71{col 34}{txt}      interval observations

{com}. 
. clear all
{txt}
{com}. set mem 30m
{txt}(30720k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. // End of variables
. 
. 
. ***Drop those subjects who don't take part in the time preference section
. drop if s2q1 == .
{txt}(596 observations deleted)

{com}. 
. ***Create the upper limit of the discount rate.
. //gen r1=.
. //replace r1= ln(ln(10.10/10)) if s2q1==1
. //gen r2=.
. //replace r2= ln(ln(10.25/10)) if s2q2==1
. //gen r3=.
. //replace r3= ln(ln(10.50/10)) if s2q3==1
. //gen r4=.
. //replace r4= ln(ln(10.75/10)) if s2q4==1
. //gen r5=.
. //replace r5= ln(ln(11/10)) if s2q5==1
. //gen r6=.
. //replace r6= ln(ln(11.25/10)) if s2q6==1
. //gen r7=.
. //replace r7= ln(ln(11.50/10)) if s2q7==1
. //gen r8=.
. //replace r8= ln(ln(12/10)) if s2q8==1
. //gen r9=.
. //replace r9= ln(ln(12.50/10)) if s2q9==1
. //gen r10=.
. //replace r10= ln(ln(13/10)) if s2q10==1
. //gen r11=.
. //replace r11= ln(ln(14/10)) if s2q11==1
. //gen r12=.
. //replace r12= ln(ln(15/10)) if s2q12==1
. 
. ***Fill in the upper limit, rounding to 7 decimal places for now vs. 1 week choices.
. gen r1=.
{txt}(406 missing values generated)

{com}. replace r1= -4.6101495 if s2q1==1
{txt}(134 real changes made)

{com}. gen r2=.
{txt}(406 missing values generated)

{com}. replace r2= -3.7012512 if s2q2==1
{txt}(163 real changes made)

{com}. gen r3=.
{txt}(406 missing values generated)

{com}. replace r3= -3.0202265 if s2q3==1
{txt}(185 real changes made)

{com}. gen r4=.
{txt}(406 missing values generated)

{com}. replace r4= -2.6266454 if s2q4==1
{txt}(195 real changes made)

{com}. gen r5=.
{txt}(406 missing values generated)

{com}. replace r5= -2.3506187 if s2q5==1
{txt}(264 real changes made)

{com}. gen r6=.
{txt}(406 missing values generated)

{com}. replace r6= -2.1389110  if s2q6==1
{txt}(270 real changes made)

{com}. gen r7=.
{txt}(406 missing values generated)

{com}. replace r7= -1.9678147 if s2q7==1
{txt}(280 real changes made)

{com}. gen r8=.
{txt}(406 missing values generated)

{com}. replace r8= -1.7019834 if s2q8==1
{txt}(316 real changes made)

{com}. gen r9=.
{txt}(406 missing values generated)

{com}. replace r9= -1.4999400 if s2q9==1
{txt}(320 real changes made)

{com}. gen r10=.
{txt}(406 missing values generated)

{com}. replace r10= -1.3380214 if s2q10==1
{txt}(353 real changes made)

{com}. gen r11=.
{txt}(406 missing values generated)

{com}. replace r11= -1.0892396 if s2q11==1
{txt}(369 real changes made)

{com}. gen r12=.
{txt}(406 missing values generated)

{com}. replace r12= -0.9027205 if s2q12==1
{txt}(390 real changes made)

{com}. 
. ***Choose the upper limit for the first time the subject makes the patient choice in the now vs. 1 week section.
. gen reservationr1= min(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12)
{txt}(16 missing values generated)

{com}. 
. ***Note that the number of missing observations for s2q1 through s2q24 (for those subjects who completed participated in the time section) is always zero.
. 
. ***Fill in the upper limit, rounding to 7 decimal places for 1 week vs. 2 week choices.
. gen r13=.
{txt}(406 missing values generated)

{com}. replace r13=-4.6101495 if s2q13==1
{txt}(163 real changes made)

{com}. gen r14=.
{txt}(406 missing values generated)

{com}. replace r14= -3.7012512 if s2q14==1
{txt}(181 real changes made)

{com}. gen r15=.
{txt}(406 missing values generated)

{com}. replace r15= -3.0202265 if s2q15==1
{txt}(199 real changes made)

{com}. gen r16=.
{txt}(406 missing values generated)

{com}. replace r16= -2.6266454 if s2q16==1
{txt}(205 real changes made)

{com}. gen r17=.
{txt}(406 missing values generated)

{com}. replace r17= -2.3506187 if s2q17==1
{txt}(276 real changes made)

{com}. gen r18=.
{txt}(406 missing values generated)

{com}. replace r18= -2.1389110 if s2q18==1
{txt}(281 real changes made)

{com}. gen r19=.
{txt}(406 missing values generated)

{com}. replace r19= -1.9678147 if s2q19==1
{txt}(294 real changes made)

{com}. gen r20=.
{txt}(406 missing values generated)

{com}. replace r20= -1.7019834 if s2q20==1
{txt}(318 real changes made)

{com}. gen r21=.
{txt}(406 missing values generated)

{com}. replace r21= -1.4999400 if s2q21==1
{txt}(323 real changes made)

{com}. gen r22=.
{txt}(406 missing values generated)

{com}. replace r22= -1.3380214 if s2q22==1
{txt}(344 real changes made)

{com}. gen r23=.
{txt}(406 missing values generated)

{com}. replace r23= -1.0892396 if s2q23==1
{txt}(361 real changes made)

{com}. gen r24=.
{txt}(406 missing values generated)

{com}. replace r24= -0.9027205 if s2q24==1
{txt}(381 real changes made)

{com}. 
. ***Choose the upper limit for the first time the subject makes the patient choice in the 1 week vs. 2 week section.
. gen reservationr2=min(r13,r14,r15,r16,r17,r18,r19,r20,r21,r22,r23, r24)
{txt}(23 missing values generated)

{com}. 
. ***Create two observations per subject where reservationr is the upper limit of the discount rate and timechoice indicates whether the choice was between now vs. 1 week or 1 week vs. 2 weeks.
. reshape long reservationr, i(id) j(timechoice)
{txt}(note: j = 1 2)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}     406   {txt}->{res}     812
{txt}Number of variables            {res}     230   {txt}->{res}     230
{txt}j variable (2 values)                     ->   {res}timechoice
{txt}xij variables:
            {res}reservationr1 reservationr2   {txt}->   {res}reservationr
{txt}{hline 77}

{com}. 
. ***Create the dummy variable week1 that takes the value 1 when the choice was between 1 week vs. 2 weeks and 0 when the choice was between now vs. 1 week.
. gen week1=0
{txt}
{com}. replace week1 = 1 if timechoice==2 
{txt}(406 real changes made)

{com}. 
. ***Recall that reservationr represents the upper limit of the discount rate. Rename this rate and then create two variables, ratel and rateu, that represent the lower and upper values for the discount rate, respectively.
. rename reservationr rate
{txt}
{com}. gen ratel=.
{txt}(812 missing values generated)

{com}. gen rateu= rate
{txt}(39 missing values generated)

{com}. 
. ***Fill in the values for ratel, the lower value for the discount rate.
. ***Note that log(x) returns the natural log of ln(x) 
. //replace ratel=. if rate==log(log(10.10/10))
. //replace ratel= log(log(10.10/10)) if rate==log(log(10.25/10))
. //replace ratel= log(log(10.25/10)) if rate==log(log(10.5/10))
. //replace ratel= log(log(10.5/10)) if rate==log(log(10.75/10))
. //replace ratel= log(log(10.75/10)) if rate==log(log(11/10))
. //replace ratel= log(log(11/10)) if rate==log(log(11.25/10))
. //replace ratel= log(log(11.25/10)) if rate==log(log(11.50/10))
. //replace ratel= log(log(11.50/10)) if rate==log(log(12/10))
. //replace ratel= log(log(12/10)) if rate==log(log(12.5/10))
. //replace ratel= log(log(12.5/10)) if rate==log(log(13/10))
. //replace ratel= log(log(13/10)) if rate==log(log(14/10))
. //replace ratel= log(log(14/10)) if rate==log(log(15/10))
. //replace ratel= log(log(15/10)) if rate==.
. 
. ***Fill in values for ratel, rounding to 7 decimal places
. ***Note that the last line takes care of those who always make the impatient choice because their discount rate lies between the highest we ask about and "."
. replace ratel=. if rateu < -4.61
{txt}(0 real changes made)

{com}. replace ratel= -4.6101494 if rateu < -3.7 & rateu > -4.61
{txt}(47 real changes made)

{com}. replace ratel= -3.7012513 if rateu < -3.02 & rateu > -3.7
{txt}(41 real changes made)

{com}. replace ratel= -3.0202265 if rateu < -2.62 & rateu > -3.02
{txt}(17 real changes made)

{com}. replace ratel= -2.6266454 if rateu < -2.35 & rateu > -2.62
{txt}(139 real changes made)

{com}. replace ratel= -2.3506187 if rateu < -2.13 & rateu > -2.35
{txt}(13 real changes made)

{com}. replace ratel= -2.1389110  if rateu < -1.96 & rateu > -2.13
{txt}(24 real changes made)

{com}. replace ratel= -1.9678147  if rateu < -1.70 & rateu > -1.96
{txt}(57 real changes made)

{com}. replace ratel= -1.7019834  if rateu < -1.49 & rateu > -1.70
{txt}(16 real changes made)

{com}. replace ratel= -1.4999400  if rateu < -1.33 & rateu > -1.49
{txt}(50 real changes made)

{com}. replace ratel= -1.3380214  if rateu < -1.08 & rateu > -1.33
{txt}(34 real changes made)

{com}. replace ratel= -1.0892396  if rateu < -0.90 & rateu > -1.08
{txt}(38 real changes made)

{com}. replace ratel= -0.9027205 if rateu==.
{txt}(39 real changes made)

{com}. 
. ***Panel E: Discount Rate
. intreg ratel rateu treatR week1 if relig==3, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-194.88149}  
Iteration 1:{space 3}log pseudolikelihood = {res: -185.3992}  
Iteration 2:{space 3}log pseudolikelihood = {res:-185.04972}  
Iteration 3:{space 3}log pseudolikelihood = {res:-185.04856}  
Iteration 4:{space 3}log pseudolikelihood = {res:-185.04856}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-193.82419}  
Iteration 1:{space 3}log pseudolikelihood = {res:-184.72991}  
Iteration 2:{space 3}log pseudolikelihood = {res:-184.42935}  
Iteration 3:{space 3}log pseudolikelihood = {res:-184.42848}  
Iteration 4:{space 3}log pseudolikelihood = {res:-184.42848}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}        80
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}      2.20
{txt}Log pseudolikelihood = {res}-184.42848{txt}{col 51}Prob > chi2{col 67}= {res}    0.3330

{txt}{ralign 78:(Std. Err. adjusted for {res:40} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .3258263{col 26}{space 2} .5705031{col 37}{space 1}    0.57{col 46}{space 3}0.568{col 54}{space 4}-.7923391{col 67}{space 3} 1.443992
{txt}{space 7}week1 {c |}{col 14}{res}{space 2}-.3701413{col 26}{space 2} .2691777{col 37}{space 1}   -1.38{col 46}{space 3}0.169{col 54}{space 4}-.8977199{col 67}{space 3} .1574374
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-3.620711{col 26}{space 2} .4386398{col 37}{space 1}   -8.25{col 46}{space 3}0.000{col 54}{space 4}-4.480429{col 67}{space 3}-2.760993
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2} .6169409{col 26}{space 2} .1093711{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .4025774{col 67}{space 3} .8313044
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2}  1.85325{col 26}{space 2} .2026921{col 54}{space 4} 1.495675{col 67}{space 3} 2.296312
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}       30{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}        0{col 34}{txt}right-censored observations
{col 24}{res}       50{col 34}{txt}      interval observations

{com}. 
. intreg ratel rateu treatR week1 if relig==4, cluster(id)

{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-439.49831}  
Iteration 1:{space 3}log pseudolikelihood = {res:-394.69249}  
Iteration 2:{space 3}log pseudolikelihood = {res: -390.5755}  
Iteration 3:{space 3}log pseudolikelihood = {res:-390.55681}  
Iteration 4:{space 3}log pseudolikelihood = {res:-390.55681}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-438.54894}  
Iteration 1:{space 3}log pseudolikelihood = {res:-394.20773}  
Iteration 2:{space 3}log pseudolikelihood = {res:-390.23328}  
Iteration 3:{space 3}log pseudolikelihood = {res:-390.21689}  
Iteration 4:{space 3}log pseudolikelihood = {res:-390.21689}  
{res}
{txt}Interval regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}      2.94
{txt}Log pseudolikelihood = {res}-390.21689{txt}{col 51}Prob > chi2{col 67}= {res}    0.2302

{txt}{ralign 78:(Std. Err. adjusted for {res:98} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0838185{col 26}{space 2} .5055551{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.9070514{col 67}{space 3} 1.074688
{txt}{space 7}week1 {c |}{col 14}{res}{space 2} -.296386{col 26}{space 2} .1729373{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.6353368{col 67}{space 3} .0425648
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.396184{col 26}{space 2} .4158531{col 37}{space 1}  -10.57{col 46}{space 3}0.000{col 54}{space 4}-5.211242{col 67}{space 3}-3.581127
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    /lnsigma{col 14}{c |}{res}{space 2} .8575153{col 26}{space 2} .0778575{col 37}{space 1}   11.01{col 46}{space 3}0.000{col 54}{space 4} .7049175{col 67}{space 3} 1.010113
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       sigma{col 14}{c |}{res}{space 2} 2.357296{col 26}{space 2} .1835331{col 54}{space 4}  2.02368{col 67}{space 3} 2.745912
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

  Observation summary:{col 24}{res}      101{col 34}{txt} left-censored observations
{col 24}{res}        0{col 34}{txt}    uncensored observations
{col 24}{res}        2{col 34}{txt}right-censored observations
{col 24}{res}       93{col 34}{txt}      interval observations

{com}. 
. clear all
{txt}
{com}. set mem 30m
{txt}(30720k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. // End of variables
. 
. rename s5 giveaway
{txt}
{com}. 
. *Panel F: Dictator Game
. reg giveaway treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      62
                                                       {txt}F(  1,    60) ={res}    0.01
                                                       {txt}Prob > F      = {res} 0.9236
                                                       {txt}R-squared     = {res} 0.0002
                                                       {txt}Root MSE      = {res} .25575

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    giveaway{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0062963{col 26}{space 2} .0653569{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.1244369{col 67}{space 3} .1370295
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}      .14{col 26}{space 2} .0435636{col 37}{space 1}    3.21{col 46}{space 3}0.002{col 54}{space 4} .0528598{col 67}{space 3} .2271402
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveaway treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     176
                                                       {txt}F(  1,   174) ={res}    2.94
                                                       {txt}Prob > F      = {res} 0.0883
                                                       {txt}R-squared     = {res} 0.0165
                                                       {txt}Root MSE      = {res} .21233

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    giveaway{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} -.054646{col 26}{space 2} .0318801{col 37}{space 1}   -1.71{col 46}{space 3}0.088{col 54}{space 4}-.1175675{col 67}{space 3} .0082755
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1651111{col 26}{space 2} .0243653{col 37}{space 1}    6.78{col 46}{space 3}0.000{col 54}{space 4} .1170216{col 67}{space 3} .2132006
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Panel G: Number of Anagrams Attempted
. reg anagramsattempted treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      33
                                                       {txt}F(  1,    31) ={res}    0.67
                                                       {txt}Prob > F      = {res} 0.4178
                                                       {txt}R-squared     = {res} 0.0211
                                                       {txt}Root MSE      = {res} 17.261

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}anagramsat~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} 4.915441{col 26}{space 2} 5.985231{col 37}{space 1}    0.82{col 46}{space 3}0.418{col 54}{space 4}-7.291517{col 67}{space 3}  17.1224
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 33.64706{col 26}{space 2} 4.475506{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4}  24.5192{col 67}{space 3} 42.77491
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg anagramsattempted treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}      93
                                                       {txt}F(  1,    91) ={res}    0.65
                                                       {txt}Prob > F      = {res} 0.4238
                                                       {txt}R-squared     = {res} 0.0069
                                                       {txt}Root MSE      = {res}  12.82

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}anagramsat~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} 2.116744{col 26}{space 2} 2.634552{col 37}{space 1}    0.80{col 46}{space 3}0.424{col 54}{space 4} -3.11647{col 67}{space 3} 7.349958
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 32.02326{col 26}{space 2} 1.781644{col 37}{space 1}   17.97{col 46}{space 3}0.000{col 54}{space 4} 28.48424{col 67}{space 3} 35.56227
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. clear all
{txt}
{com}. set mem 50m
{txt}(51200k)

{com}. set more off
{txt}
{com}. 
. use "/Users/gwf25/Dropbox/research/religion/final code/religion_all.dta"
{txt}
{com}. 
. //  Begin definition of variables
. 
. ***Drop subjects who thought the experiment was about religion
. gen id = _n
{txt}
{com}. drop if id == 98 | id == 176 | id == 194 | id == 383
{txt}(4 observations deleted)

{com}. 
. ***Drop subjects who incorrectly completed the priming task. This includes subjects who leave more than half the responses blank. The following subjects
. ***all left at least questions #2-#7 blank in the sentence unscrambling task. 
. drop if id==7 | id==719 | id==740 | id==762 | id==940
{txt}(5 observations deleted)

{com}. 
. ***An error led to some subjects seeing both the control and religion salient sentence unscrambling tasks. Here, we drop those subjects.
. drop if prime_diff == 1
{txt}(2 observations deleted)

{com}. 
. ***"skipped" is a dummy variable for whether subjects skip the question that asks their religion. If they skip this question, we drop them from the sample 
. ***and if not, we assign a dummy variable to indicate the treatment group (religion salient or control) that subject belongs to.
. gen skipped=0
{txt}
{com}. replace skipped=1 if  s10q15==""
{txt}(21 real changes made)

{com}. gen treatR=.
{txt}(1033 missing values generated)

{com}. replace treatR=religion if skipped==0
{txt}(1012 real changes made)

{com}. drop if skipped==1
{txt}(21 observations deleted)

{com}. 
. ***Define religion
. gen relig=.
{txt}(1012 missing values generated)

{com}. 
. ***Note: 1 = protestant or other christian, 2 = catholic, 3 = jewish, 4 = agnostic/atheist
. replace relig=1 if (s10q15=="Christian - Other (please specify below)" | s10q15=="Christian - Protestant (please specify denomination below)")
{txt}(264 real changes made)

{com}. replace relig=2 if s10q15=="Christian - Catholic"
{txt}(199 real changes made)

{com}. replace relig=3 if s10q15=="Jewish (Orthodox/Reformed/etc.)" | s10q15=="Jewish (Orthodox/Reform/etc.)"
{txt}(95 real changes made)

{com}. replace relig=4 if (s10q15=="Agnostic" | s10q15=="Atheist")
{txt}(269 real changes made)

{com}. 
. ***Drop Mormon/Othodox Christians from the sample
. drop if s10q15sp == "Greek Orthodox"
{txt}(2 observations deleted)

{com}. drop if s10q15sp == "Russian Othrodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "greek orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthdox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "christian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox Christian"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Russian orthodox"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Church of Jesus Christ of Latter Day Saints"
{txt}(1 observation deleted)

{com}. drop if s10q15sp == "Greek Orthodox"
{txt}(0 observations deleted)

{com}. 
. drop id
{txt}
{com}. // End of variables
. 
. 
. rename m wageoffer
{txt}
{com}. 
. ***Panel I: wage offered as manager
. reg wageoffer treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      40
                                                       {txt}F(  1,    38) ={res}    2.14
                                                       {txt}Prob > F      = {res} 0.1521
                                                       {txt}R-squared     = {res} 0.0525
                                                       {txt}Root MSE      = {res} .91086

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   wageoffer{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .4185464{col 26}{space 2} .2863894{col 37}{space 1}    1.46{col 46}{space 3}0.152{col 54}{space 4}-.1612186{col 67}{space 3} .9983113
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9761905{col 26}{space 2}  .211486{col 37}{space 1}    4.62{col 46}{space 3}0.000{col 54}{space 4} .5480595{col 67}{space 3} 1.404322
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg wageoffer treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     113
                                                       {txt}F(  1,   111) ={res}    2.28
                                                       {txt}Prob > F      = {res} 0.1340
                                                       {txt}R-squared     = {res} 0.0202
                                                       {txt}Root MSE      = {res} .96846

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   wageoffer{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.2755326{col 26}{space 2} .1825352{col 37}{space 1}   -1.51{col 46}{space 3}0.134{col 54}{space 4}-.6372382{col 67}{space 3}  .086173
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.570175{col 26}{space 2}  .115016{col 37}{space 1}   13.65{col 46}{space 3}0.000{col 54}{space 4} 1.342263{col 67}{space 3} 1.798087
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Create the cost, cXXX, for providing a certain amount of effort at eXXX. A certain monetary cost is associated with each level of output. So, we have:
. ***[eXXX, cXXX] = [(1,$0.00),(2,$0.04),(3,$0.08),(4,$0.16),(5,$0.24),(6,$0.32),(7,$0.40),(8,$0.48),(9,$0.60),(10,$0.72)]
. gen c000 = .
{txt}(1002 missing values generated)

{com}. replace c000 = 0 if e000 == 1
{txt}(423 real changes made)

{com}. replace c000 = .04 if e000 == 2
{txt}(2 real changes made)

{com}. replace c000 = .08 if e000 == 3
{txt}(0 real changes made)

{com}. replace c000 = .16 if e000 == 4
{txt}(1 real change made)

{com}. replace c000 = .24 if e000 == 5
{txt}(0 real changes made)

{com}. replace c000 = .32 if e000 == 6
{txt}(0 real changes made)

{com}. replace c000 = .40 if e000 == 7
{txt}(0 real changes made)

{com}. replace c000 = .48 if e000 == 8
{txt}(0 real changes made)

{com}. replace c000 = .60 if e000 == 9
{txt}(0 real changes made)

{com}. replace c000 = .72 if e000 == 10
{txt}(1 real change made)

{com}. 
. gen c050 = .
{txt}(1002 missing values generated)

{com}. replace c050 = 0 if e050 == 1
{txt}(315 real changes made)

{com}. replace c050 = .04 if e050 == 2
{txt}(78 real changes made)

{com}. replace c050 = .08 if e050 == 3
{txt}(25 real changes made)

{com}. replace c050 = .16 if e050 == 4
{txt}(3 real changes made)

{com}. replace c050 = .24 if e050 == 5
{txt}(2 real changes made)

{com}. replace c050 = .32 if e050 == 6
{txt}(0 real changes made)

{com}. replace c050 = .40 if e050 == 7
{txt}(1 real change made)

{com}. replace c050 = .48 if e050 == 8
{txt}(0 real changes made)

{com}. replace c050 = .60 if e050 == 9
{txt}(2 real changes made)

{com}. replace c050 = .72 if e050 == 10
{txt}(1 real change made)

{com}. 
. gen c100 = .
{txt}(1002 missing values generated)

{com}. replace c100 = 0 if e100 == 1
{txt}(216 real changes made)

{com}. replace c100 = .04 if e100 == 2
{txt}(68 real changes made)

{com}. replace c100 = .08 if e100 == 3
{txt}(97 real changes made)

{com}. replace c100 = .16 if e100 == 4
{txt}(31 real changes made)

{com}. replace c100 = .24 if e100 == 5
{txt}(11 real changes made)

{com}. replace c100 = .32 if e100 == 6
{txt}(0 real changes made)

{com}. replace c100 = .40 if e100 == 7
{txt}(2 real changes made)

{com}. replace c100 = .48 if e100 == 8
{txt}(0 real changes made)

{com}. replace c100 = .60 if e100 == 9
{txt}(1 real change made)

{com}. replace c100 = .72 if e100 == 10
{txt}(1 real change made)

{com}. 
. gen c150 = .
{txt}(1002 missing values generated)

{com}. replace c150 = 0 if e150 == 1
{txt}(181 real changes made)

{com}. replace c150 = .04 if e150 == 2
{txt}(41 real changes made)

{com}. replace c150 = .08 if e150 == 3
{txt}(56 real changes made)

{com}. replace c150 = .16 if e150 == 4
{txt}(75 real changes made)

{com}. replace c150 = .24 if e150 == 5
{txt}(58 real changes made)

{com}. replace c150 = .32 if e150 == 6
{txt}(8 real changes made)

{com}. replace c150 = .40 if e150 == 7
{txt}(5 real changes made)

{com}. replace c150 = .48 if e150 == 8
{txt}(1 real change made)

{com}. replace c150 = .60 if e150 == 9
{txt}(1 real change made)

{com}. replace c150 = .72 if e150 == 10
{txt}(1 real change made)

{com}. 
. gen c200 = .
{txt}(1002 missing values generated)

{com}. replace c200 = 0 if e200 == 1
{txt}(168 real changes made)

{com}. replace c200 = .04 if e200 == 2
{txt}(25 real changes made)

{com}. replace c200 = .08 if e200 == 3
{txt}(31 real changes made)

{com}. replace c200 = .16 if e200 == 4
{txt}(42 real changes made)

{com}. replace c200 = .24 if e200 == 5
{txt}(82 real changes made)

{com}. replace c200 = .32 if e200 == 6
{txt}(32 real changes made)

{com}. replace c200 = .40 if e200 == 7
{txt}(20 real changes made)

{com}. replace c200 = .48 if e200 == 8
{txt}(22 real changes made)

{com}. replace c200 = .60 if e200 == 9
{txt}(3 real changes made)

{com}. replace c200 = .72 if e200 == 10
{txt}(2 real changes made)

{com}. 
. gen c250 = .
{txt}(1002 missing values generated)

{com}. replace c250 = 0 if e250 == 1
{txt}(166 real changes made)

{com}. replace c250 = .04 if e250 == 2
{txt}(17 real changes made)

{com}. replace c250 = .08 if e250 == 3
{txt}(28 real changes made)

{com}. replace c250 = .16 if e250 == 4
{txt}(18 real changes made)

{com}. replace c250 = .24 if e250 == 5
{txt}(53 real changes made)

{com}. replace c250 = .32 if e250 == 6
{txt}(65 real changes made)

{com}. replace c250 = .40 if e250 == 7
{txt}(33 real changes made)

{com}. replace c250 = .48 if e250 == 8
{txt}(14 real changes made)

{com}. replace c250 = .60 if e250 == 9
{txt}(6 real changes made)

{com}. replace c250 = .72 if e250 == 10
{txt}(27 real changes made)

{com}. 
. gen c300 = .
{txt}(1002 missing values generated)

{com}. replace c300 = 0 if e300 == 1
{txt}(159 real changes made)

{com}. replace c300 = .04 if e300 == 2
{txt}(14 real changes made)

{com}. replace c300 = .08 if e300 == 3
{txt}(15 real changes made)

{com}. replace c300 = .16 if e300 == 4
{txt}(26 real changes made)

{com}. replace c300 = .24 if e300 == 5
{txt}(27 real changes made)

{com}. replace c300 = .32 if e300 == 6
{txt}(45 real changes made)

{com}. replace c300 = .40 if e300 == 7
{txt}(51 real changes made)

{com}. replace c300 = .48 if e300 == 8
{txt}(39 real changes made)

{com}. replace c300 = .60 if e300 == 9
{txt}(11 real changes made)

{com}. replace c300 = .72 if e300 == 10
{txt}(40 real changes made)

{com}. 
. gen c350 = .
{txt}(1002 missing values generated)

{com}. replace c350 = 0 if e350 == 1
{txt}(160 real changes made)

{com}. replace c350 = .04 if e350 == 2
{txt}(11 real changes made)

{com}. replace c350 = .08 if e350 == 3
{txt}(15 real changes made)

{com}. replace c350 = .16 if e350 == 4
{txt}(17 real changes made)

{com}. replace c350 = .24 if e350 == 5
{txt}(36 real changes made)

{com}. replace c350 = .32 if e350 == 6
{txt}(16 real changes made)

{com}. replace c350 = .40 if e350 == 7
{txt}(34 real changes made)

{com}. replace c350 = .48 if e350 == 8
{txt}(54 real changes made)

{com}. replace c350 = .60 if e350 == 9
{txt}(37 real changes made)

{com}. replace c350 = .72 if e350 == 10
{txt}(47 real changes made)

{com}. 
. gen c400 = .
{txt}(1002 missing values generated)

{com}. replace c400 = 0 if e400 == 1
{txt}(231 real changes made)

{com}. replace c400 = .04 if e400 == 2
{txt}(7 real changes made)

{com}. replace c400 = .08 if e400 == 3
{txt}(8 real changes made)

{com}. replace c400 = .16 if e400 == 4
{txt}(9 real changes made)

{com}. replace c400 = .24 if e400 == 5
{txt}(22 real changes made)

{com}. replace c400 = .32 if e400 == 6
{txt}(11 real changes made)

{com}. replace c400 = .40 if e400 == 7
{txt}(15 real changes made)

{com}. replace c400 = .48 if e400 == 8
{txt}(27 real changes made)

{com}. replace c400 = .60 if e400 == 9
{txt}(31 real changes made)

{com}. replace c400 = .72 if e400 == 10
{txt}(66 real changes made)

{com}. 
. ***Drop the highest effort amount
. drop c400
{txt}
{com}. 
. ***In order to properly reshape the data, we must rename the effort and cost of effort at $0 and $0.50
. rename e000 e0
{txt}
{com}. rename e050 e50
{txt}
{com}. rename c000 c0
{txt}
{com}. rename c050 c50
{txt}
{com}. 
. ***Create the corresponding wages for each effort choice
. gen w0 = 0
{txt}
{com}. gen w50 = .5
{txt}
{com}. gen w100 = 1
{txt}
{com}. gen w150 = 1.5
{txt}
{com}. gen w200 = 2
{txt}
{com}. gen w250 = 2.5
{txt}
{com}. gen w300 = 3
{txt}
{com}. gen w350 = 3.5
{txt}
{com}. 
. ***Create the estimated regression parameter where beta=reciprocity
. gen beta=.
{txt}(1002 missing values generated)

{com}. 
. gen id = _n
{txt}
{com}. 
. ***Reshape the data so that we can regress cost of effort on all possible wages, by subject
. reshape long c, i(id) j(w)
{txt}(note: j = 0 50 100 150 200 250 300 350)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1002   {txt}->{res}    8016
{txt}Number of variables            {res}     221   {txt}->{res}     215
{txt}j variable (8 values)                     ->   {res}w
{txt}xij variables:
                        {res}c0 c50 ... c350   {txt}->   {res}c
{txt}{hline 77}

{com}. 
. ***Loop through all subjects and regress the effort on wage by each subject and save values of regression
. ***Note that some subjects did not do this section so we exclude them here. Only subjects numbered 263 through 689 did the gift exchange task.
. quiet forvalues x = 263/689 {c -(}
{txt}
{com}. 
. ***Reshape the data to its original form
. reshape wide c, i(id) j(w)
{txt}(note: j = 0 50 100 150 200 250 300 350)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}    8016   {txt}->{res}    1002
{txt}Number of variables            {res}     215   {txt}->{res}     221
{txt}j variable (8 values)                 {res}w   {txt}->   (dropped)
xij variables:
                                      {res}c   {txt}->   {res}c0 c50 ... c350
{txt}{hline 77}

{com}. 
. ***Panel H: slope from reg of work cost on wages
. ***Note: remember to multiply point estimates and standard errors by 100 to get the value in dollars. This scaling is a result of wages being displayed in cents and not dollars. 
. reg beta treatR if relig == 3, r

{txt}Linear regression                                      Number of obs ={res}      40
                                                       {txt}F(  1,    38) ={res}    4.91
                                                       {txt}Prob > F      = {res} 0.0328
                                                       {txt}R-squared     = {res} 0.1171
                                                       {txt}Root MSE      = {res} .00084

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        beta{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2} .0005985{col 26}{space 2} .0002701{col 37}{space 1}    2.22{col 46}{space 3}0.033{col 54}{space 4} .0000516{col 67}{space 3} .0011454
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0004717{col 26}{space 2} .0001587{col 37}{space 1}    2.97{col 46}{space 3}0.005{col 54}{space 4} .0001503{col 67}{space 3}  .000793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg beta treatR if relig == 4, r

{txt}Linear regression                                      Number of obs ={res}     113
                                                       {txt}F(  1,   111) ={res}    1.70
                                                       {txt}Prob > F      = {res} 0.1946
                                                       {txt}R-squared     = {res} 0.0151
                                                       {txt}Root MSE      = {res} .00083

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        beta{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatR {c |}{col 14}{res}{space 2}-.0002045{col 26}{space 2} .0001567{col 37}{space 1}   -1.30{col 46}{space 3}0.195{col 54}{space 4}-.0005151{col 67}{space 3}  .000106
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0009375{col 26}{space 2} .0001209{col 37}{space 1}    7.76{col 46}{space 3}0.000{col 54}{space 4}  .000698{col 67}{space 3}  .001177
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/gwf25/Desktop/Table 4.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Dec 2015, 10:07:32
{txt}{.-}
{smcl}
{txt}{sf}{ul off}